most citedWhen Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Models

3 citations · 3 across the 17 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

cs.AI2026

Mitigating Reasoning-Induced Misalignment via Safety-Direction Penalty

Yipeng Zhao, Qishun Yang, Shenzhe Zhu +2

Reasoning-Induced Misalignment, where fine-tuning on reasoning data containing no harmful content, including mathematics, code, and problem-solving with chain-of-thought traces can…

cs.AI2026

Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability

Xinyan Jiang, Ninghao Liu, Di Wang +1

Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework that assesses reasoning quality th…

cs.AI2025

PIXEL: Adaptive Steering Via Position-wise Injection with eXact Estimated Levels under Subspace Calibration

Manjiang Yu, Hongji Li, Priyanka Singh +3

Reliable behavior control is central to deploying large language models (LLMs) on the web. Activation steering offers a tuning-free route to align attributes (e.g., truthfulness) t…

cs.AI2025

When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMs

Zhuoran Zhang, Tengyue Wang, Xilin Gong +4

Multimodal large language models (MLLMs) must resolve conflicts when different modalities provide contradictory information, a process we term modality following. Prior work measur…

cs.AI2025

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images

Liangliang You, Junchi Yao, Shu Yang +3

While multimodal large language models excel at various tasks, they still suffer from hallucinations, which limit their reliability and scalability for broader domain applications.…